(734) 615-1915

Complex Systems, Computer Science, Economics, Electrical Engineering, Networks, Operations Research
Bayesian Methods, Causal Inference, Classification, Computational Tools for Data Science, Dynamical Models, High-Dimensional Data Analysis, Information Theory, Mathematics, Network Analysis, Optimization, Statistical Inference, Statistical Modeling, Statistics
Relevant Projects:


Vijay Subramanian

Associate Professor

EECS, College of Engineering

Professor Subramanian is interested in a variety of stochastic modeling, decision and control theoretic, and applied probability questions concerned with networks. Examples include analysis of random graphs, analysis of processes like cascades on random graphs, network economics, analysis of e-commerce systems, mean-field games, network games, telecommunication networks, load-balancing in large server farms, and information assimilation, aggregation and flow in networks especially with strategic users.